Convert simple LSTM model to hls
Created by: AnouarITI
I want to map a very simple LSTM model to the ZCU104. First I added the the board support to the .json file and added the template of the board similarly to the ZCU104. Then I started with the following code.
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import matplotlib.pyplot as plt
from pandas import read_csv
import math
from keras.models import Sequential
from keras.layers import Dense
from keras.layers import LSTM
from sklearn.preprocessing import MinMaxScaler
from sklearn.metrics import mean_squared_error
df = pd.read_csv('dataset.csv')
dataset = df.iloc[:,1].values
dataset = dataset.reshape(-1,1)
dataset = dataset.astype('float32') # (144, 1)
scaler = MinMaxScaler(feature_range=(0,1))
dataset = scaler.fit_transform(dataset)
timestamp=10
dataX = []
dataY = []
for i in range(len(train)-timestamp-1):
a = train[i:(i+timestamp),0]
dataX.append(a)
dataY.append(train[i+timestamp,0])
trainX = np.array(dataX)
trainY = np.array(dataY)
timestep=10
dataX = []
dataY = []
for i in range(len(test)-timestamp-1):
a = test[i:(i+timestamp),0]
dataX.append(a)
dataY.append(test[i+timestamp,0])
testX = np.array(dataX)
testY = np.array(dataY)
trainX = np.reshape(trainX,(trainX.shape[0],1,trainX.shape[1]))
testX = np.reshape(testX,(testX.shape[0],1,testX.shape[1]))
model = Sequential()
model.add(LSTM(10, input_shape=(1, timestamp))) # 10 lstm neuron(block)
model.add(Dense(1))
model.compile(loss='mean_squared_error', optimizer='adam')
model.fit(trainX, trainY, epochs=50, batch_size=1)
trainPredict = model.predict(trainX)
testPredict = model.predict(testX)
trainPredict = scaler.inverse_transform(trainPredict)
trainY = scaler.inverse_transform([trainY])
testPredict = scaler.inverse_transform(testPredict)
testY = scaler.inverse_transform([testY])
trainScore = math.sqrt(mean_squared_error(trainY[0], trainPredict[:,0]))
print('Train Score: %.2f RMSE' % (trainScore))
testScore = math.sqrt(mean_squared_error(testY[0], testPredict[:,0]))
print('Test Score: %.2f RMSE' % (testScore))
import hls4ml
import os
os.environ['PATH'] = '/home/user/Xilinx/Vivado/2020.1/bin:' + os.environ['PATH']
hls4ml.model.optimizer.get_optimizer('output_rounding_saturation_mode').configure(layers=['Activation'])
hls4ml.model.optimizer.get_optimizer('output_rounding_saturation_mode').configure(rounding_mode='AP_RND')
hls4ml.model.optimizer.get_optimizer('output_rounding_saturation_mode').configure(saturation_mode='AP_SAT')
hls_config = hls4ml.utils.config_from_keras_model(model,
granularity='model',
default_precision="ap_fixed<8,2>",
default_reuse_factor=1)
print(hls_config)
hls_model = hls4ml.converters.convert_from_keras_model(model,
io_type="io_stream",
hls_config=hls_config,
output_dir='lstm_model/hls4ml_8b_rf1',
project_name="lstm_hls",
backend='VivadoAccelerator',
part='xczu7ev-ffvc1156-2-e')Here I get the following error, which I fail to understand:
Interpreting Sequential
Topology:
Layer name: lstm_input, layer type: InputLayer, input shapes: [[None, 1, 10]], output shape: [None, 1, 10]
Layer name: lstm, layer type: LSTM, input shapes: [[None, 1, 10]], output shape: [None, 10]
Layer name: dense, layer type: Dense, input shapes: [[None, 10]], output shape: [None, 1]
Creating HLS model
---------------------------------------------------------------------------
KeyError Traceback (most recent call last)
Cell In [23], line 1
----> 1 hls_model = hls4ml.converters.convert_from_keras_model(model,
2 io_type="io_stream",
3 hls_config=hls_config,
4 output_dir='lstm_model/hls4ml_8b_rf1',
5 project_name="lstm_hls",
6 backend='VivadoAccelerator',
7 part='xczu7ev-ffvc1156-2-e')
File ~/.local/lib/python3.8/site-packages/hls4ml/converters/__init__.py:217, in convert_from_keras_model(model, output_dir, project_name, input_data_tb, output_data_tb, backend, hls_config, **kwargs)
213 config['HLSConfig']['Model'] = _check_model_config(model_config)
215 _check_hls_config(config, hls_config)
--> 217 return keras_to_hls(config)
File ~/.local/lib/python3.8/site-packages/hls4ml/converters/keras_to_hls.py:374, in keras_to_hls(config)
369 #################
370 ## Generate HLS
371 #################
373 print('Creating HLS model')
--> 374 hls_model = ModelGraph(config, reader, layer_list, input_layers, output_layers)
375 return hls_model
File ~/.local/lib/python3.8/site-packages/hls4ml/model/graph.py:319, in ModelGraph.__init__(self, config, data_reader, layer_list, inputs, outputs)
316 self._make_graph(layer_list)
318 for flow in self.config.flows:
--> 319 self.apply_flow(flow)
File ~/.local/lib/python3.8/site-packages/hls4ml/model/graph.py:368, in ModelGraph.apply_flow(self, flow, reapply)
365 return
367 self._applied_flows.append(applied_flows)
--> 368 self._apply_sub_flow(flow, applied_flows)
File ~/.local/lib/python3.8/site-packages/hls4ml/model/graph.py:377, in ModelGraph._apply_sub_flow(self, flow_name, applied_flows)
375 for sub_flow in flow.requires:
376 if sub_flow not in applied_flows.keys():
--> 377 self._apply_sub_flow(sub_flow, applied_flows)
379 if len(flow.optimizers) > 0:
380 applied_passes = optimize_model(self, flow.optimizers)
File ~/.local/lib/python3.8/site-packages/hls4ml/model/graph.py:380, in ModelGraph._apply_sub_flow(self, flow_name, applied_flows)
377 self._apply_sub_flow(sub_flow, applied_flows)
379 if len(flow.optimizers) > 0:
--> 380 applied_passes = optimize_model(self, flow.optimizers)
381 else:
382 applied_passes = set()
File ~/.local/lib/python3.8/site-packages/hls4ml/model/optimizer/optimizer.py:286, in optimize_model(model, passes)
284 for node in model.graph.values():
285 if opt.match(node):
--> 286 res = opt.transform(model, node)
287 applied_passes.add(opt_name)
288 if res:
File ~/.local/lib/python3.8/site-packages/hls4ml/backends/template.py:20, in Template.transform(self, model, node)
19 def transform(self, model, node):
---> 20 formatted_template = self.format(node)
21 node.set_attr(self.attribute_name, formatted_template)
22 return False
File ~/.local/lib/python3.8/site-packages/hls4ml/backends/vivado/passes/recurrent_templates.py:99, in RecurrentConfigTemplate.format(self, node)
97 params['act_t'] = '{}_config{}'.format(node.get_attr('activation'), node.index)
98 params['strategy'] = node.get_attr('strategy')
---> 99 params['static'] = 'true' if node.attributes['static'] else 'false'
100 params['recr_type'] = node.class_name.lower()
101 params['RECR_TYPE'] = node.class_name
File ~/.local/lib/python3.8/site-packages/hls4ml/model/attributes.py:54, in AttributeDict.__getitem__(self, key)
53 def __getitem__(self, key):
---> 54 return self.attributes[key]
KeyError: 'static'Any help please I need it urgently